如今，, 任何拥有足够大的人工智能模型的人都可以在几分钟内生成数百万种新材料设计。不幸的是，, 没有’t 导致用于提高计算机芯片和火箭等产品性能的新材料数量的巨大飞跃。

Today, anyone with a large enough artificial intelligence model can generate millions of new material designs in minutes. Unfortunately, that hasn’t led to a huge leap in the number of new materials being used to improve the performance of products like computer chips and rockets.

翻译差距的原因之一是当前模型’t 无法可靠地考虑其生成的材料的化学稳定性,，而不稳定的材料’t 在现实世界中非常有用。这迫使各行业分配大量的计算预算来筛选掉它们产生的所有不稳定材料,，在某些情况下留下一小部分可用的选择。

One reason for the translation gap is that current models don’t reliably factor in the chemical stability of the materials they generate, and unstable materials aren’t very useful in the real world. That forces industries to allocate huge computational budgets to screening out all the unstable materials they generate, in some cases leaving behind a tiny fraction of usable options.

现在, 麻省理工学院的研究人员开发了一个框架，可以在材料生成过程的开始阶段应用，以极大地提高稳定性，同时实现目标材料特性。它的工作原理是，在昂贵的生成步骤开始之前，确保每个设计都满足与材料’原子周围的电子相关的某些化学关键规则。研究人员将他们的方法称为具有价态约束设计的“晶体发生器,或CrysVCD。

Now, MIT researchers have developed a framework that can be applied at the beginning of the materials generation process to vastly improve the stability rate while achieving targeted material properties. It works by ensuring every design satisfies certain key rules of chemistry relating to the electrons around the materials atoms before the expensive generation step begins. The researchers call their approach “crystal generator with valence-constrained design, or CrysVCD.

在今天发表在《自然计算科学》, 上的一篇论文中，研究人员展示了 CrysVCD 如何允许几种常用的材料模型更频繁地满足这些价壳规则,，并使用它在近 70% 的计算材料代中实现高晶格动力学稳定性 — 和严格的稳定性测试 —。他们还表明，该方法可以支持创建具有特定所需属性, 的材料，例如高导热率或高介电常数,，这对于计算机芯片和数据中心非常重要。

In a paper published today in Nature Computational Science, the researchers show how CrysVCD allowed several commonly used material models to meet those valence shell rules more often, and used it to achieve high lattice-dynamics stability — a stringent stability test — in nearly 70 percent of computational material generations. They also showed the approach could support the creation of materials with specific desired properties, like high thermal conductivity or high dielectric constant, which is important for computer chips and data centers.

从名字中就可以看出研究人员设想人们如何使用他们的系统。

A hint of how the researchers envision people using their system is in the name.

“如果材料生成模型像 DVD,，我们就像 DVD 播放器,” 核科学与工程副教授李明达说。 “您可以将其插入任何类型的模型,不仅是现有的扩散模型，而且还可以是未来的模型,，人们可以’t生成足够稳定的材料,并且它可以提高稳定性。”

“If material-generating models are like DVDs, we are like the DVD player,” says associate professor of nuclear science and engineering Mingda Li. “You can plug this into any kind of model, not only existing diffusion models but also future models, where people can’t generate enough stable materials, and it can improve stability.”

材料设计的计算方法已经存在了几十年,，但人工智能的最新进展让人们对其潜力更加兴奋。特别令人感兴趣的是可以从所需的材料属性开始并向后工作以提供实现该目标的材料的模型。

Computational approaches to materials design have been around for decades, but recent advances in artificial intelligence have increased excitement about their potential. Of particular interest are models that can start with a desired material property and work backward to deliver a material that achieves that goal.

其中一些模型使用一种称为扩散,的人工智能技术，该技术通常用于生成图像,，而其他模型则使用大型语言模型，例如为 ChatGPT 和 Claude, 提供支持的模型，但这两种方法都难以确保其材料生成实现化学稳定性或遵循有关化学物质如何相互作用和行为的基本原则。

Some of those models use an AI technique known as diffusion, which is commonly used to generate images, while others use large language models like the one powering ChatGPT and Claude, but both approaches struggle to ensure their material generations achieve chemical stability or follow fundamental principles about how chemicals interact and behave.

解决方案是在生成过程之上添加另一层计算，以过滤掉不稳定的材料。

The solution has been to add another layer of computing on top of the generative process to filter out unstable materials.

“It的 变得容易生成材料结构,” Cheng 说。 “但是验证过程,尤其是测试稳定性,的部分具有巨大的计算成本。 的 大约 90% 的计算成本用于创建可用材料,，并且可能需要数周或数月的时间。”

“It的 becoming easy to generate the material structure,” Cheng says. “But the validation process, especially the part where you test the stability, has a huge computational cost. It的 something like 90 percent of the computational cost for creating usable materials, and it can take weeks or months.”

拥有巨额计算预算的大公司有能力运行这些流程,，但许多小公司和研究实验室可以’t, 可能会限制该领域的创新。

Big companies with huge computing budgets can afford to run those processes, but many small companies and research labs can’t, potentially limiting innovation in the field.

“在学术界,，我们的资源较少, 我认为我们仍然可以通过更智能的设计和其他方法实现强大的性能,” Kulik 解释道。 “生成模型然后向下选择以获得稳定性效率很低。计算成本很高。但如果我们在过程开始时放置一个语言模型来约束,的生成，则可以显着提高稳定材料生成的比例。”

“In academia, where we have fewer resources, I think we can still achieve strong performance with smarter designs and other approaches,” Kulik explains. “Generating a model and then down-selecting for stability is inefficient. There的 a high computational cost. But if we put a language model in the beginning of the process to constrain the generation, you can significantly enhance the ratio of stable materials generated.”

这项新研究涉及麻省理工学院材料科学与工程,化学,化学工程,物理,和核科学与工程系的研究人员。研究人员共同将人工智能扩散模型与语言模型结合起来。在他们的过程的第一阶段,语言模型产生化学上有效的公式。在第二阶段,中，扩散模型使用该公式来生成与底层材料生成模型相协调的晶体材料的相应原子结构。

The new study involved MIT researchers affiliated with the departments of Materials Science and Engineering, Chemistry, Chemical Engineering, Physics, and Nuclear Science and Engineering. Together the researchers combined AI diffusion models with a language model. In the first stage of their process, the language model produces chemically valid formulas. In the second stage, the diffusion model uses that formula to generate the corresponding atomic structure of the crystal material in coordination with the underlying material generation model.

“D 典型材质生成的扩散是一个缓慢的过程 — 您可以将其视为创建一种材质的 1,000 个步骤,” Luo 说。

“Diffusion for typical material generation is a slow process — you can think of it like 1,000 steps to create one material,” Luo says.

“相反,当我们的模型在开始时使用,你可以把它想象成五个步骤。它可以让您筛选出不稳定的材料，以生成更高质量的材料。它适用于任何生成材料的模型,” Tang 补充道。

“In contrast, when our model is used in the beginning, you can think of it like five steps. It allows you to screen out the unstable materials to generate higher quality materials. And it works with any models generating materials,” Tang adds.

研究人员表明，他们的方法创造出更稳定的材料，比依赖材料生成后筛选材料的方法效率高一个数量级。当对稳定性指标, 进行微调时，他们的方法生产出的晶体材料实现了 68% 的机械稳定性和 85% 的亚稳定性,，用于衡量材料在不受干扰时是否保持稳定状态。

The researchers showed their approach created more stable materials an order of magnitude more efficiently than approaches that rely on screening materials after they’re generated. When fine-tuned on stability metrics, their approach produced crystalline materials that achieved 68 percent mechanical stability and 85 percent metastability, which measures if a material stays in a stable state when undisturbed.

然后，研究人员利用他们的方法生成了具有高导热性和在电场中容易极化的候选材料。

The researchers then used their approach to generate material candidates with high thermal conductivity and easy polarization in an electric field.

“这些是可用于半导体行业的材料以及与数据中心冷却相关的高导热材料,” Ju Li 说。 “原则上,您还可以使用它来创建其他属性,，但导热性对于冷却数据中心已经变得非常重要。该行业的能源使用量 大幅增加,，其中 30% 用于冷却。行业需要高导热率的材料来更有效地散发热量。”

“These are materials useful for the semiconductor industry and high thermal conductivity materials relevant to data center cooling,” Ju Li says. “In principle, you could also use this to create other properties, but thermal conductivity has become really important for cooling data centers. There的 been a huge increase in energy use in that industry, and 30 percent of that energy goes to cooling. The industry needs materials with high thermal conductivity to more efficiently remove the heat.”

新方法不适用于’t 每种材料— 它最适合具有高度有序内部排列的实体结构。尽管如此，, 该方法可用于生成具有许多重要特性的稳定的新型晶体材料。

The new approach doesn’t work with every kind of material — it works best with solid structures with highly ordered internal arrangements. Still, the approach could be used to generate stable new crystalline materials with a host of important properties.

“我们不仅生产稳定的材料,，我们’还优先考虑性能,” Cheng说。 “任何时候你有两个目标,在这个领域，以超过 50% 的比例实现这些目标是很困难的。在过去,，人们可能有特定性能的目标，而不是稳定性,，反之亦然,，并获得符合其目标的材料的个位数百分比。”

“We are not just generating stable materials, we’re also prioritizing performance,” Cheng says. “Any time you have two goals, achieving those goals with anything over 50 percent is hard in this field. In the past, people might have a goal for specific properties and not stability, or vice-versa, and get a single-digit percentage of materials that fit their goal.”

最终，该方法将使更多研究人员能够为一系列下一代应用开发新型材料。

Ultimately the approach will enable more researchers to develop novel materials for a range of next-generation applications.